The exemplary embodiment relates to color management in a printing system and finds particular application in connection with a system which combines conventional scanners with an in-line-spectrometer for color management in a duplex printing device.
Systems that perform inline color management for printing devices generally use an in-line spectrophotometer (ILS) to measure the spectral response of the printer for a given set of patches. The results may be used for color profiling or color calibration of the printing device. The ILS measures the color (e.g., in a device independent color space, such as L*a*b*) at one or more locations in a cross-process direction of the printing device. Inline spectrophotometers tend to be expensive and generally require tight specifications for mounting since each location uses a separate spectrophotometer component. For a duplex printing device, that prints on both sides of a sheet with separate marking engines, an ILS may be used for each side, increasing the cost further. One method to eliminate the need for an ILS is to train an RGB scanner to provide good spectral estimation using the measured mean RGB values of a patch. This provides reasonable spectral estimation, but the scanner accuracy is inferior to the spectrophotometer, which may measure 31 spectral value per patch, rather than three. Several machine learning-based training methods (e.g., using Gaussian regression) can give relatively good results nearing those of an ILS (1.5 ΔE2000, 95%)), but the performance of these methods is only good for the paper type and ink type that used during training occurred. More general techniques can be used over a wider array of papers and inks, but the errors associated with these methods is much larger (i.e. 4-5 ΔE2000, 95%). While a ΔE is generally considered an imperceptible color difference, higher values are generally associated with noticeable differences. ΔE2000 (or ΔE2k) is a color difference measure which measures the distance between two colors. This measure varies the weighting of L*, depending on where in the lightness range the color falls. 95% indicates the 95th percentile. See for example, G. Sharma and S. Wang, “Spectrum recovery from colorimetric data for color reproductions,” in Proc. SPIE, Color Imaging: Device-Independent Color, Color Hardcopy, and Applications VII, vol. 4663, San Jose, Calif., 2002; G. Sharma, S. Wang, D. Sidavanahalli, and K. T. Knox, “The impact of UCR on scanner calibration,” in Final Prog. and Proc. IS&T's PICS Conf., pp. 121-124, Portland, Oreg., 1998; R. Bala, “Device characterization,” Chapter 5 of Digital Color Imaging Handbook, edited by G. Sharma, CRC Press, 2003. P. G. Roetling, J. E. Stinehour, and M. S. Maltz, “Color characterization of a scanner,” in Proc. IS&T 7th Intl. Congress on Non-Impact Printing, pp. 443-451, 1991, for canner calibration methods.